An Organization-Wide Approach to Good Decision Making

Behavioral economists and psychologists have uncovered scores of biases that undermine good decision-making. And, along with management experts, they have provided helpful tips that decision-makers can use to try to correct for those biases. But a comprehensive framework for achieving quality decision-making throughout an organization is still rare — almost three-quarters of companies have no formal corporate-wide approach to making major, complex decisions.

Without a proven, organization-wide approach, there may be, at best, isolated pockets of high-quality decision-making where individual leaders have elected to take a rigorous, transparent approach. Otherwise, the organization is at the mercy of the biggest bias of all: the perception that it is good at making decisions.

With an organization-wide approach you can increase the odds of circumventing that bias. Further, an organization-wide common language speeds up the making and reviewing of decisions. Transparency in how decisions are reached replaces the blind faith that people must place in the judgment of their superiors. Most importantly, more high quality decisions, instead of merely “good enough” decisions, together can add up to billions of dollars in additional value.

The first step is to define what a good decision looks like. In the early 1990s, Chevron (where until recently one of us worked) began experimenting with Decision Quality (DQ), a process that defines a high-quality decision as the course of action that will capture the most value or get the most of what you are seeking, given the uncertainties and complexities of the real world.

Armed with that definition, Chevron has applied the tools of Decision Analysis (DA), to the choices it faces. DA rejects the all-too-common approach of deciding between the status quo and a single alternative course of action. Instead, DA involves considering a range of possible outcomes, their probability of occurring, and the results (financial or otherwise) of each. Decision-makers can then compare alternatives in terms of both upside opportunity and downside risk, and make decisions in light of their own appetite for risk and tolerance of uncertainty.

With these methods in mind, we can describe the six elements of DQ that characterize any high-quality decision:

An appropriate frame, including a clear understanding of the problem and what needs to be achieved.

Creative, doable alternatives from which to choose the one likely to achieve the most of what you want.

Meaningful information that is reliable, unbiased, and reflects all relevant uncertainties and intangibles.

Clarity about desired outcomes, including acceptable tradeoffs.

Solid reasoning and sound logic that includes considerations of uncertainty and insight at the appropriate level of complexity.

Commitment to action by all stakeholders necessary to achieve effective action.

DQ won a foothold in Chevron through some eye-catching early successes in some major capital decisions. For example, one of the company’s refineries needed upgrading to remain competitive in the refining business. A first pass at the problem resulted in a proposal to install a unit called a flexi-coker — a major system capable of refining a range of crude oil types and minimizing the ultra-heavy coke residual.

During the installation, the refinery would have to be shut down, resulting in a significant loss of revenue. Flexi-cokers are expensive, and related improvements proposed for implementation during the shutdown would add to the project’s cost. As details of the engineered design matured, the estimated cost escalated, nearly doubling to $2 billion. At that point, senior management asked for a more rigorous review to better understand the risks and risk/reward balance.

To revisit the issue, the team tasked with the review turned to DQ concepts. In the course of the DQ process, the frame was widened to include all improvements that could be made during shutdown of the facility, including those unrelated to the flexi-coker. Doable alternatives, excluding the flexi-coker, were explored. The team then analyzed what would happen if certain variables like feedstock crude oil price, wholesale gasoline prices, and project duration deviated more than had been expected originally. And they analyzed the probability of these specific deviations occurring. The team was then able to articulate the overall risk profile of the project: the potential costs for each deviation and the likelihood of each occurring. These methods revealed that the risk profile of the project as originally conceived was more than Chevron’s management was willing to accept.

A set of acceptable cost/value trade-offs, and the likelihood of achieving them, was determined. Further analysis revealed that it was the proposed new flexi-coker itself that was contributing the bulk of the downside risk. At this point, Chevron’s management publicly announced the shelving of the flexi-coker portion of the project.

Chevron estimates that revisiting the decision and the subsequent change in project scope captured more than 50% of the original projected value at 25% of the cost, regaining the competitive position of the refinery at a much lower level of deployed capital.

For the first 10 years, the use of these decision techniques by various groups in Chevron was voluntary, though there were a string of early successes. During that time, Chevron proceeded to build deep internal competence in the discipline – introducing thousands of decision-makers to DQ and DA and developing hundreds of internal decision support professionals who applied them to many major decisions.

When David O’Reilly became Chairman and CEO in 2000, he insisted that DQ become mandatory on all capital expenditures over $50 million. Decision executives were also required to become certified in the fundamentals of the approach. Decision professionals were embedded in the organization around the world, and became part of the Chevron capital stewardship process.

The impact of many thousands of people making even marginally better decisions was huge in a company Chevron’s size. And in operations, where decisions are made almost daily, the result was, in effect, continuous improvement.

Achieving that level of decision quality throughout your organization takes some work, but you will quickly find that it has great appeal, apart from the value it captures through consistently higher-value decisions. Decision-makers and teams are energized by its capacity to get people to agree on a decision, though they may have begun with widely divergent views about the best course of action.

However, as anyone who has ever seen groupthink in action knows, any number of otherwise intelligent people can come to agree on nonsense. Conversely, autocratic leaders can simply impose their will. But by first defining what constitutes a high quality decision and the elements that go into it, DQ/DA erases lines drawn in the sand by intransigent team members.

Your teams are freed to focus on each element of this rational decision-making model and identify gaps in the quality of a decision. Instead of sticking to biases or getting mired in politics, people work to fill those gaps, with analytics providing a clear line of sight to the most value. Further, by satisfying all six elements of DQ, companies can recognize the quality of a decision as they make it, not just in hindsight. The result: far fewer failed strategies, far less wasted capital in investment decisions, and — to everyone’s great relief — fewer blame games and witch hunts.

The hard truth is we all leave a lot of value on the table – value that we could seize with better decisions. Doing so requires an organization-wide framework for making them.

Larry Neal recently retired as a manager of Decision Analysis at Chevron Corporation.